def plot_preset(preset_name, preset, save=False, close=True):
plt.figure()
plt.suptitle('Preset: {} (N = {})'.format(preset_name, preset.N))
plt.subplot(2, 1, 1)
plt.ylabel('Phase histogram')
phase_range = (0, 2.0 * math.pi)
plt.xlim(phase_range[0], phase_range[1])
plt.hist(preset.phase, bins=50, range=phase_range)
plt.subplot(2, 1, 2)
plt.ylabel('Frequency histogram')
freq_range = (np.percentile(preset.freq, 5), np.percentile(preset.freq, 95))
d = freq_range[1] - freq_range[0]
freq_range = (freq_range[0] - 0.15*d, freq_range[1] + 0.15*d)
plt.xlim(freq_range[0], freq_range[1])
plt.hist(preset.freq, bins=50, range=freq_range)
if save:
save_plot(preset_name, close=False)
else:
plt.show()
if close:
plt.close()
def plot_step(params):
name = params['name']
#preset = params['preset']
step = params['step']
f_name = params['f_name']
dir_name = params['dir_name']
preset = load_preset_from_file(name)
freq = preset.freq
with open(f_name, 'r') as f:
lines = f.readlines()
step_, N, r, mean = (x for x in lines[0].split())
step_ = int(step_)
assert (step_ == step)
N = int(N)
r = float(r)
mean = float(mean)
phases = [float(x) for x in lines[1].split()]
vel = [float(x) for x in lines[2].split()]
#print len(phases), len(vel)
print(step)
#for i in xrange(N):
# pos = (phases[i], freq[i])
# print pos
plt.figure()
plt.suptitle('Step: ' + str(step))
plt.subplot(2, 1, 1)
#py.axvline(95)
#py.axvline(35)
#plt.xlabel('Phase')
plt.ylabel('Phase histogram')
plt.hist(phases, bins=60, range=(0, 2.0 * math.pi))
plt.xlim(0, 2.0 * math.pi)
plt.subplot(2, 1, 2)
#plt.xlabel('Velocity')
plt.ylabel('Velocity histogram')
#range = (np.min(vel), np.max(vel))
range = (-30, 30)
plt.hist(vel, bins=60, range=range)
plt.xlim(range[0], range[1])
save_plot(os.path.join(dir_name, 'hist', str(step)))
plt.figure()
plt.title('Step: ' + str(step))
plt.xlabel('Phase')
plt.ylabel('Intrinsic frequency')
plt.xlim(0, 2.0 * math.pi)
plt.ylim(-3, 3)
plt.plot(phases, freq, marker='o', ls='')
save_plot(os.path.join(dir_name, 'phase', str(step)))
def plot_step(params):
name = params['name']
#preset = params['preset']
step = params['step']
f_name = params['f_name']
dir_name = params['dir_name']
preset = load_preset_from_file(name)
freq = preset.freq
with open(f_name, 'r') as f:
lines = f.readlines()
step_, N, r, mean = (x for x in lines[0].split())
step_ = int(step_)
assert(step_ == step)
N = int(N)
r = float(r)
mean = float(mean)
phases = [float(x) for x in lines[1].split()]
vel = [float(x) for x in lines[2].split()]
#print len(phases), len(vel)
print(step)
#for i in xrange(N):
# pos = (phases[i], freq[i])
# print pos
plt.figure()
plt.suptitle('Step: ' + str(step))
plt.subplot(2, 1, 1)
#py.axvline(95)
#py.axvline(35)
#plt.xlabel('Phase')
plt.ylabel('Phase histogram')
plt.hist(phases, bins=60, range=(0, 2.0 * math.pi))
plt.xlim(0, 2.0 * math.pi)
plt.subplot(2, 1, 2)
#plt.xlabel('Velocity')
plt.ylabel('Velocity histogram')
#range = (np.min(vel), np.max(vel))
range = (-30, 30)
plt.hist(vel, bins=60, range=range)
plt.xlim(range[0], range[1])
save_plot(os.path.join(dir_name, 'hist', str(step)))
plt.figure()
plt.title('Step: ' + str(step))
plt.xlabel('Phase')
plt.ylabel('Intrinsic frequency')
plt.xlim(0, 2.0 * math.pi)
plt.ylim(-3, 3)
plt.plot(phases, freq, marker='o', ls='')
save_plot(os.path.join(dir_name, 'phase', str(step)))
def tree_all(tree,
plane='xy',
feature='radial_distances',
title='',
diameter=True,
treecol='b',
xlims=None,
ylims=None,
**kwargs):
'''Subplot with ph, barcode and tree
'''
from tmd import utils as _utils
from matplotlib.collections import LineCollection
kwargs['output_path'] = kwargs.get('output_path', None)
fig1, ax1 = _view.tree(tree,
new_fig=True,
subplot=221,
plane='xy',
title=title,
treecolor=treecol,
diameter=diameter)
feat = getattr(tree, 'get_section_' + feature)()
segs = tree.get_segments()
def _seg_2d(seg):
"""2d coordinates required for the plotting of a segment"""
horz = _utils.term_dict[plane[0]]
vert = _utils.term_dict[plane[1]]
horz1 = seg[0][horz]
horz2 = seg[1][horz]
vert1 = seg[0][vert]
vert2 = seg[1][vert]
return ((horz1, vert1), (horz2, vert2))
if plane in ['xy', 'yx', 'zx', 'xz', 'yz', 'zy']:
ph = _tm.methods.get_persistence_diagram(tree, feature=feature)
else:
raise Exception('Plane value not recognised')
bounds = max(max(ph))
fig1, ax2 = ph_diagram(ph, new_fig=False, subplot=222, color=treecol)
fig1, ax3 = barcode(ph, new_fig=False, subplot=223, color=treecol)
fig1, ax4 = ph_image(ph,
new_fig=False,
subplot=224,
xlims=xlims,
ylims=ylims)
_cm.plt.tight_layout(True)
if kwargs['output_path'] is not None:
fig = _cm.save_plot(fig=ax1, **kwargs)
return fig1, ax1
def plot_crit_k(preset_name, K_values, r_mean, save=True, close=True):
plt.figure()
plt.xlabel('k')
plt.ylabel('r')
plt.xlim(np.min(K_values), np.max(K_values))
plt.ylim(0, 1)
plt.plot(K_values, r_mean)
# TODO: plot theoretical value
if save:
save_plot(os.path.join('dump_' + preset_name, 'crit_k'))
else:
plt.show()
if close:
plt.close()
def plot_crit_k(preset_name, K_values, r_mean, save=True, close=True):
plt.figure()
plt.xlabel('k')
plt.ylabel('r')
plt.xlim(np.min(K_values), np.max(K_values))
plt.ylim(0, 1)
plt.plot(K_values, r_mean)
# TODO: plot theoretical value
if save:
save_plot(os.path.join('dump_' + preset_name, 'crit_k'))
else:
plt.show()
if close:
plt.close()
def gen_mean_and_r_plots(dir_name):
with open(os.path.join(dir_name, 'r.txt')) as f:
r = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Order parameter')
plt.xlim(0, len(r))
plt.ylim(0, 1)
plt.plot(range(0, len(r)), r)
save_plot(os.path.join('dump_' + name, 'r'))
with open(os.path.join(dir_name, 'mean.txt')) as f:
mean = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Mean phase')
plt.xlim(0, len(mean))
plt.ylim(0, 2.0 * math.pi)
plt.plot(range(0, len(mean)), mean)
save_plot(os.path.join('dump_' + name, 'mean'))
with open(os.path.join(dir_name, 'mean_vel.txt')) as f:
mean_vel = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Mean velocity')
plt.xlim(0, len(mean_vel))
plt.plot(range(0, len(mean_vel)), mean_vel)
save_plot(os.path.join('dump_' + name, 'mean_vel'))
def gen_mean_and_r_plots(dir_name):
with open(os.path.join(dir_name, 'r.txt')) as f:
r = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Order parameter')
plt.xlim(0, len(r))
plt.ylim(0, 1)
plt.plot(range(0, len(r)), r)
save_plot(os.path.join('dump_' + name, 'r'))
with open(os.path.join(dir_name, 'mean.txt')) as f:
mean = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Mean phase')
plt.xlim(0, len(mean))
plt.ylim(0, 2.0 * math.pi)
plt.plot(range(0, len(mean)), mean)
save_plot(os.path.join('dump_' + name, 'mean'))
with open(os.path.join(dir_name, 'mean_vel.txt')) as f:
mean_vel = [float(x) for x in f.read().split()]
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Mean velocity')
plt.xlim(0, len(mean_vel))
plt.plot(range(0, len(mean_vel)), mean_vel)
save_plot(os.path.join('dump_' + name, 'mean_vel'))
def neu_all(neuron,
plane='xy',
feature='radial_distances',
title='',
diameter=True,
treecol='b',
xlims=None,
ylims=None,
neurite_type='basal',
**kwargs):
'''Subplot with ph, barcode
and tree within spheres
'''
from tmd import utils as _utils
from matplotlib.collections import LineCollection
kwargs['output_path'] = kwargs.get('output_path', None)
fig1, ax1 = _view.neuron(neuron,
new_fig=True,
subplot=221,
plane='xy',
neurite_type=[neurite_type],
title=title,
treecolor=treecol,
diameter=diameter)
if plane in ['xy', 'yx', 'zx', 'xz', 'yz', 'zy']:
ph = _tm.methods.get_ph_neuron(neuron,
feature=feature,
neurite_type=neurite_type)
else:
raise Exception('Plane value not recognised')
bounds = max(max(ph))
fig1, ax2 = ph_diagram(ph, new_fig=False, subplot=222, color=treecol)
fig1, ax3 = barcode(ph, new_fig=False, subplot=223, color=treecol)
fig1, ax4 = ph_image(ph,
new_fig=False,
subplot=224,
xlims=xlims,
ylims=ylims)
_cm.plt.tight_layout(True)
if kwargs['output_path'] is not None:
fig = _cm.save_plot(fig=ax1, **kwargs)
return fig1, ax1
plt, ax, fig = common.get_pyplot_ax_fig()
ax.grid(True, color='xkcd:light grey')
ax.set_axisbelow(True) # ensure grid is below data
lines = []
for (folder, lats) in values.items():
(color, label, marker) = common.get_color_label_marker(
folder
) # this works with folder names due to how we determine this info...
x = lats
y = [(float(n + 1) / (len(lats) + 1)) for n in range(len(lats))]
ax.plot(x, y, color=color, label=label)
plt.legend(loc='upper right',
handletextpad=0.3,
borderaxespad=0,
facecolor='white',
framealpha=1,
edgecolor='white')
plt.yscale('log', basey=10, nonposy='mask')
# Primorac et al. 2017, beyond 99.99th percentile NIC timestamp accuracy is not reliable
ax.set_ylim(bottom=0.0001,
top=1.03) # 1.03 so the top line doesn't get cut off
ax.set_xlim(lat_min, lat_9999th)
plt.xlabel('Latency (\u03BCs)')
plt.ylabel('CCDF')
common.save_plot(plt, name)
print("Done! Plot is in ../plots/" + name + ".svg")